Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling

@article{Luengo2011AddressingDC,
  title={Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling},
  author={Juli{\'a}n Luengo and Alberto Fern{\'a}ndez and Salvador Garc{\'i}a and Francisco Herrera},
  journal={Soft Comput.},
  year={2011},
  volume={15},
  pages={1909-1936}
}
In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a preprocessing step. In this paper we analyze the usefulness of the data complexity measures in order… CONTINUE READING
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Diagnose of effective evolutionary prototype selection using an overlapping measure

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